LLMs

2026-09-18

RESEARCH · 1 SOURCE · MIT Technology Review AI

MIT Technology Review roundtable: Could advanced AI destroy humanity?

MIT Technology Review hosted a roundtable discussion (available to watch or listen) examining claims by some employees at leading AI labs that advanced AI could pose an existential threat to humanity, exploring where those fears come from and how plausible they are. The session unpacks arguments for and against AI-driven extinction without asserting a definitive conclusion.

6.0

2026-09-16

COMPANIES · 1 SOURCE · Cohere

Cohere publishes 'Enterprise AI Maturity Model' outlining five-phase path from pilots to AI-native firms

Cohere outlines a five-phase Enterprise AI Maturity Model—Experimentation, Tool adoption, Internal platforms, Strategic integrations, and AI-native transformation—and argues most organizations stall between Tool adoption and Internal platforms; it highlights barriers to moving pilots into production including siloed data access, trust and compliance concerns with LLMs, and fear of model obsolescence.

5.0

2026-09-15

RESEARCH · 1 SOURCE · arXiv cs.AI

Continual Search: iterative approach improves root-cause attribution for long-horizon agent failures

New arXiv paper (arXiv:2609.13463v1) frames root-cause attribution (RCA) for long-horizon agent failures as a large search problem and introduces Continual Search, an iterative framework that prompts LLM-based judges to repeatedly search for unresolved diagnostic evidence. The authors also release MegaRCA-Mix, a 50-trial benchmark of long-horizon, execution-heavy failures, and report that Continual Search boosts attribution performance across benchmarks—for example improving GPT-5.5's F1 from 0.349 to 0.498 (over 40%).

7.0

2026-09-14

MODELS · 1 SOURCE · InfoQ AI, ML & Data Engineering

Integrating DMN Decision Models, LLMs and NeMo to Produce Auditable Agentic Architectures

Alex Porcelli outlines a response to a key enterprise AI problem—non-deterministic outputs and lack of accountability—by describing how combining DMN decision models with LLMs, agent skills and NeMo guardrails can yield auditable, deterministic agentic architectures that let business leaders own decision logic while engineers retain architectural governance.

6.0

2026-08-25

MODELS · 1 SOURCE · GitHub AI & ML

GitHub: How to evaluate LLMs before production

GitHub's blog published a post titled “How to evaluate LLMs before production” that describes lessons the team learned while evaluating large language models for a real-world secret-scanning system. The post is presented as practical guidance for assessing LLM behavior and suitability prior to deployment.

6.0